Introduction

In Chapter 1, Fundamentals, and Chapter 2, Exploratory Data Analysis and Visualization, we introduced the concept of supervised machine learning in Python and the essential techniques required for loading, cleaning, exploring, and visualizing raw data sources. We discussed the importance of fully understanding the data before moving on to further analysis, as well as how the initial data preparation process can sometimes account for the majority of the time spent on the project as a whole. In particular, we considered correlations among all the variables, finding and addressing missing values, and understanding the shape of data via histograms, bar plots, and density plots. In this chapter, we will delve into the model building process and will construct our first supervised machine learning solution using linear regression.